text: #
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  from . import generic_dataset
  
  
  class BaseTask:
      """
      Base class for all tasks.
  
      Includes all the code to handle generic tasks
  
      """
  
      def __init__(self, name, args):
          self.name = name
          # special task-specific tokens that should not be subword tokenized
          self.special_tokens = set()
          self.override_context = args.override_context
          self.override_question = args.override_question
  
      @property
      def default_question(self):
          return ''
  
      @property
      def default_context(self):
          return ''
  
      def get_splits(self, root, **kwargs):
          """
          Load the train, test, eval datasets for this task
  
          :param field: the text.Field to use for tokenization, preprocessing and vocabulary construction
          :param root: the base directory where data is stored
          :param kwargs: other arguments to pass to the Dataset
          :return: a list of text.Dataset
          """
          return generic_dataset.JSON.splits(root=root, name=self.name, **kwargs)
  
      def postprocess_prediction(self, example_id, prediction):
          return prediction
  
      def preprocess_field(self, sentence, field_name=None):
          if self.override_context is not None and field_name == 'context':
              return self.override_context
          if self.override_question is not None and field_name == 'question':
              return self.override_question
          return sentence
  
      @property
      def metrics(self):
          """
          What metrics to evaluate this task on.
  
          This property must return a non-empty list.
          The first entry in the list will be the metric to use to compute the decascore.
  
          :return: a list of metric names
          """
          return ['em', 'nem', 'nf1']
  